Recommendation SystemConsumer FacingVerified

Reviewed and published by trentmaziarz, March 23, 2026. Discovered and drafted by our automated research pipeline.

Pinterest uses Pinnability, a machine learning system, to rank every Pin shown on each user's home feed based on predicted engagement. The system has been in place since 2015, replacing a chronological ordering of content, and is continuously updated; the most recent major version, TransActV2, was published as a production model in June 2025 and extends personalization to up to 16,000 historical user actions per person.

Details

The system operates in two stages. First, a retrieval layer uses over 20 candidate generators — including embedding-based nearest-neighbor search across billions of Pins — to assemble a pool of potentially relevant content for each user. Then a ranking layer uses deep neural networks to score each candidate using combinations of Pin signals, long-term user preference embeddings (mathematical representations of a user's tastes built from their entire save history), and real-time signals reflecting what the user has done in the current session. TransActV2 extended the historical action window from 100 to 16,000 actions per user, running on custom GPU kernels to maintain response times under 100 milliseconds.

Products affected

Pinterest home feed

Sources & Evidence

News Article

https://arxiv.org/abs/2506.02267

News Article

https://www.businessofapps.com/data/pinterest-statistics/

Company Disclosure

Company DisclosureVerified

Next-Level Personalization: How 16k+ Lifelong User Actions Supercharge Pinterest’s Recommendations

Pinterest Engineering Blog ·Jun 2025

https://medium.com/pinterest-engineering/next-level-personalization-how-16k-lifelong-user-actions-supercharge-pinterests-recommendations-bd5989f8f5d3

Company DisclosureVerified

How Pinterest Leverages Realtime User Actions in Recommendation to Boost Homefeed Engagement Volume

Pinterest Engineering Blog·Nov 2022

https://medium.com/pinterest-engineering/how-pinterest-leverages-realtime-user-actions-in-recommendation-to-boost-homefeed-engagement-volume-165ae2e8cde8

Company DisclosureVerified

Pinnability: Machine learning in the home feed

Pinterest Engineering Blog·Mar 2015

https://medium.com/pinterest-engineering/pinnability-machine-learning-in-the-home-feed-64be2074bf60

Cite this record

Trace Foundation. (2026). Pinterest: Pinterest uses Pinnability, a machine learning system, to rank every Pin shown on each user's home feed based on predicted engagement. The system has been in place since 2015, replacing a chronological ordering of content, and is continuously updated; the most recent major version, TransActV2, was published as a production model in June 2025 and extends personalization to up to 16,000 historical user actions per person (data as of 2026-06-06) [Data set record]. AI Trace. https://www.aitrace.org/r/practice/85c08334-e2b1-4cf5-b209-46022053904c. Accessed October 14, 2026.

Stable link: https://www.aitrace.org/r/practice/85c08334-e2b1-4cf5-b209-46022053904c
Data as of: June 6, 2026
Last verified: March 23, 2026

How to cite AI Trace

Other practices by Pinterest

OtherPinterest maintains an internal program called Responsible AI that covers three areas: building inclusive AI features (the skin tone, body type, and hair pattern tools described in Practice 21), testing its AI systems for unfair bias (using internal tools that measure whether models perform equally well across different groups of users), and developing guidelines for the safe use of generative AI. The program includes red teaming — structured attempts to find flaws or harmful outputs in AI systems before they launch.OtherPinterest is testing AI-powered upgrades to its Boards feature that go beyond simple user-organized collections. In October 2025, Pinterest launched an experiment in the U.S. and Canada where the platform automatically generates outfit ideas from a user's saved fashion Pins ("Styled for you"), creates entirely AI-curated boards based on trending styles ("Boards made for you"), and suggests related products based on what a user has already saved ("Make It Yours").OtherPinterest uses AI to identify diverse skin tones, body types, and hair patterns across billions of images on its platform, then uses those signals to ensure that search results and recommendations reflect a broader range of human appearances. Users can filter fashion search results by body type ranges, skin tone ranges, and hair patterns. The body type technology launched in September 2023, with public user filters rolling out in March 2024; skin tone ranges have been available since 2018.

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